By Frank Emmert-Streib, Matthias Dehmer

ISBN-10: 3527318224

ISBN-13: 9783527318223

This ebook is the 1st to target the applying of mathematical networks for interpreting microarray facts. this system is going way past the normal clustering equipment routinely used.

From the contents:

  • Understanding and Preprocessing Microarray information
  • Clustering of Microarray facts
  • Reconstruction of the Yeast phone Cycle by way of Partial Correlations of upper Order
  • Bilayer Verification set of rules
  • Probabilistic Boolean Networks as types for Gene legislation
  • Estimating Transcriptional Regulatory Networks by way of a Bayesian community
  • Analysis of healing Compound results
  • Statistical equipment for Inference of Genetic Networks and Regulatory Modules
  • Identification of Genetic Networks via Structural Equations
  • Predicting useful Modules utilizing Microarray and Protein interplay info
  • Integrating effects from Literature Mining and Microarray Experiments to deduce Gene Networks

The ebook is for either, scientists utilizing the process in addition to these constructing new research thoughts.

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Additional resources for Analysis of Microarray Data: A Network-Based Approach

Example text

And Wold, B. (2007) Genome-wide mapping of in vivo protein–DNA interactions. Science, 316 (5830), 1497–1502. , Vogelstein, B. W. (1997) Characterization of the yeast transcriptome. Cell, 88 (2), 243–251. MGED Society (2002) Microarray standards at last. Nature, 419, 323. , Domrachev, M. E. (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 30 (1), 207–210. , Farne, A. , Kapushesky, M. , Sansone, S. and, Brazma, A. (2005) ArrayExpress – a public repository for microarray gene expression data at the EBI.

In this regard, the relatively new emergence of tiling arrays has prompted the development of new methods for normalization [77]. Current methods do not take sequence composition into account, yet is clear that sequence effects will contribute to hybridization signals and should be accounted for. Affymetrix GeneChips use hybridization intensities of single samples as a readout of gene expression. Since many factors unrelated to gene expression can affect the hybridization properties of a probe, each gene is represented not by one probe (like most other types of arrays) but by a population of probes.

M. and Spencer, F. (2004) A Model Based Background Adjustment for Oligonucleotide Expression Arrays. Johns Hopkins University Department of Biostatistics Working Papers. Working Paper 1. E. F. (2006) Evaluation of methods for oligonucleotide array data via quantitative real-time PCR. BMC Bioinformatics, 177, 23. References 82 Zhou, L. M. (2005) An expression index for Affymetrix GeneChips based on the generalized logarithm. Bioinformatics, 21 (21), 3983–3989. M. S. (2005) Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset.

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